A Neural Machine Translation Model for Arabic Dialects That Utilises Multitask Learning (MTL).

Area of Science:

• Natural Language Processing
• Machine Learning
• Computational Linguistics

Background:

• Neural machine translation (NMT) models, particularly recurrent neural network (RNN)-based encoder-decoder architectures, have advanced machine translation by framing it as a sequence learning problem.
• Translating diverse Arabic dialects into Modern Standard Arabic (MSA) presents unique challenges due to linguistic variations.

Purpose of the Study:

• To develop and evaluate a multi-task learning (MTL) model for Arabic dialect translation to MSA.
• To address the challenge of limited data availability in dialect translation tasks.

Main Methods:

• The proposed model utilizes a multi-task learning (MTL) framework with a shared decoder across different language pairs and individual encoders for each source dialect.
• The architecture is based on the recurrent neural network (RNN)-based encoder-decoder NMT model.

Main Results:

• Experimental results demonstrate that the proposed MTL model achieves higher translation quality compared to models trained individually for each dialect.
• The MTL model performs effectively on both limited and extensive datasets.

Conclusions:

• The developed MTL model offers a superior approach for translating Arabic dialects to MSA.
• This method shows promise for improving NMT performance in low-resource scenarios.